# statsmodels.genmod.families.family.Binomial¶

Binomial exponential family distribution.

Parameters
link`a` `link` `instance`, `optional`

`statsmodels.genmod.families.family.Family`

Notes

endog for Binomial can be specified in one of three ways: A 1d array of 0 or 1 values, indicating failure or success respectively. A 2d array, with two columns. The first column represents the success count and the second column represents the failure count. A 1d array of proportions, indicating the proportion of successes, with parameter var_weights containing the number of trials for each row.

Attributes
Binomial.link`a` `link` `instance`

The link function of the Binomial instance

Binomial.variance`varfunc` `instance`

`variance` is an instance of statsmodels.genmod.families.varfuncs.binary

Methods

Methods

 `deviance`(endog, mu[, var_weights, ...]) The deviance function evaluated at (endog, mu, var_weights, freq_weights, scale) for the distribution. `fitted`(lin_pred) Fitted values based on linear predictors lin_pred. `get_distribution`(mu[, scale, var_weights, ...]) Frozen Binomial distribution instance for given parameters `initialize`(endog, freq_weights) Initialize the response variable. `loglike`(endog, mu[, var_weights, ...]) The log-likelihood function in terms of the fitted mean response. `loglike_obs`(endog, mu[, var_weights, scale]) The log-likelihood function for each observation in terms of the fitted mean response for the Binomial distribution. Linear predictors based on given mu values. `resid_anscombe`(endog, mu[, var_weights, scale]) The Anscombe residuals `resid_dev`(endog, mu[, var_weights, scale]) The deviance residuals The starting values for the IRLS algorithm for the Binomial family. Weights for IRLS steps

Properties

 `link` Link function for family `links` `safe_links` `valid` `variance`